The Many Paths to Data Science

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.
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PD

I thought this course introduced the topic of data science very well. I think I have a much better idea how to describe data science and common terms associated with the field (like machine learning).

DS

Aug 14, 2019

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Descriptive picture of data science. Videos are short but nicely presented which gives an student a clear idea of the subject. Even Documents at the end of the course presentation are well explained.

수업에서

Defining Data Science and What Data Scientists Do

In this module, you will go over the course syllabus to learn what will be taught in this course. Also, you will hear from data science professionals to learn what data science is, what data scientists do, and what tools and algorithms data scientists use on a daily basis. Finally, you will be required to complete a reading assignment to learn why data science is considered the sexiest job in the 21st century.

강사:

Alex Aklson

Ph.D., Data Scientist

Polong Lin

Data Scientist

스크립트

[SOUND] [MUSIC] Data science didn't really exist when I was growing up. It's not something that I ever woke up and said, I want to be a data scientist when I grow up. No, it didn't exist. I didn't know I would be working in data science. >> When I grew up, there isn't that field called data science. And I think it's really new. >> Data science didn't exist until 2009, 2011. Someone like DJ Patil or Andrew Gelman coined the term. Before that, there was statistics. And I didn't want to be any of those. I wanted to be in business. And then I found data science a heck of a lot more interesting. >> I studied statistics, that's how I started. I went through many different stages in my life where I wanted to be a singer and then a doctor. And then I realized that I was good at math. So I chose an area that was focused on quantitative analysis. And from then I do think that I wanted to work with data. Not necessarily data science as it's known today. >> The first time that I had contact with data science, when I was my first year as a mechanical engineering. And strategic consulting firms, they use data science to make decisions. So it was my first contact with data science. >> I had a complicated problem that I needed to solve, and the usual techniques that we had at the time couldn't help with that problem. >> I graduated with a math degree in the worst possible time, right after the economic crisis, and you actually had to be useful to get a job. So I went and got a degree in statistics. And then I worked enough jobs that were called data scientist that I suddenly became one. >> My undergraduate degree was in business, and I majored in politics, philosophy, and economics. And then I did a master's in business analytics at New York University at the Stern School of Business. When I left my undergrad, the first company I joined, it turned out that they were analyzing electronic point of sale data for retail manufacturers. And what we were doing was data science. But we only really started using that term much later. In fact, I'd say four or five years ago is when we started calling it analytics and data science. >> I had several options for my internship here in Canada. And one of the options was to work with data science. I used to work with project development. But I think that was a good choice. And then I start my internship with data science. >> I'm a civil engineer by training, so all engineers work with data. I would say the conventional use of data science in my life started with transportation research. I started building large models trying to forecast traffic on streets, trying to determine congestion and greenhouse gas emissions or tailpipe emissions. So I think that's where my start was. And I started building these models when I was a graduate student at the University of Toronto. Started working with very large data sets, looking at household samples of, say, 150,000 households from half a million trips. And that, too, I'm speaking from mid 90s when this was supposed to be a very large data set, but not in today's terms. But that's how I started. I continued working with it. And then I moved to McGill University where I was a professor of transportation engineering. And I built even bigger data models that involved data and analytics. And so I would say, yes, transportation research brought me to data science. [MUSIC]